3D Piping Route Layout Using AI Vector Routing Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The design and implementation of complex piping systems in industrial plants, such as power generation systems, are cumbersome and often result in non-optimized routes, increasing design time and operational inefficiencies.
Innovation Solution
A three-dimensional (3-D) piping layout is generated using artificial intelligence-driven training and modeling, involving a scale increasing model and vector routing model to optimize pipe routes based on various criteria, which can be iteratively updated with feedback for improved efficiency and practicality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design methods are used for piping routes, then design flexibility is maintained, but design time and complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical design processes with an AI-based automated system. The processing circuitry executes machine learning models to generate and optimize piping routes automatically, substituting human manual design work with computational algorithms that can evaluate multiple route options simultaneously and optimize based on multiple criteria including cost, time, and operational efficiency.
2Productivity
If traditional piping design methods are used, then implementation simplicity is maintained, but route optimization is insufficient
Solution Approach 1:
The system implements feedback mechanisms where the AI model continuously evaluates generated piping routes against multiple optimization criteria and iteratively improves the solutions. The processing circuitry analyzes route performance, compares against constraints and objectives, and refines the piping layouts to achieve optimal balance between operational efficiency and implementation feasibility.
Solution Approach 2:
The patent utilizes parameter changes by adjusting multiple design variables simultaneously including pipe dimensions, route coordinates, material selections, and layout configurations. The AI system varies these parameters iteratively to explore the design space and identify optimal piping routes that satisfy both operational efficiency requirements and implementation constraints.
3Loss of time
If repetitive redesign is performed manually, then adaptability to changes is maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-defining multiple route criteria, constraints, and optimization objectives before the actual route generation process. The AI model is trained on historical data and pre-configured with project-specific requirements, enabling it to rapidly generate optimized routes when design changes occur, eliminating the need for time-consuming manual redesign iterations.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
A system (116) includes a processing circuitry (212) and a memory (122), accessible by the processing circuitry (212), the memory (122) storing instructions that, when executed by the processing circuitry (212) cause the processing circuitry (212) to perform operation including receiving one or more route criteria (244) and identifying a number of pipes, wherein the number of pipes is based on the one or more route criteria (244). The piping also includes generating a vector route (402), storing the vector route (402), generating a limiting zone (422), and performing an iterative process including determining one or more vector routes (402) for each of the identified number of pipes. Further, the piping system (116) also includes optimizing a route solution based on the one or more vector routes (402) based on an optimization parameter and outputting a three-dimensional pipe layout (242), wherein the three-dimensional pipe layout (242) is transmitted to an external platform for display via a user interface (208).